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最新推荐文章于 2025-12-16 10:44:31 发布

原创最新推荐文章于 2025-12-16 10:44:31 发布 · 350 阅读

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#python

部署运行你感兴趣的模型镜像

from keras import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dropout
from keras.models import Model
from keras.layers import concatenate
from tensorflow.keras.optimizers import Adam
from keras.layers import Conv2DTranspose
from keras.layers import (Conv2D, MaxPooling2D, Dropout, concatenate,
Conv2DTranspose, BatchNormalization, Activation)
import os
import numpy as np
import tensorflow as tf
from keras import Input
from keras.layers import Conv2D, MaxPooling2D, Dropout, concatenate, Conv2DTranspose
from keras.models import Model
from tensorflow.keras.optimizers import Adam

def enhancednet(pretrained_weights=None):
input_shape = (None, None, 1)
inputs = Input(shape=input_shape, name='input_img')
conv1 = Conv2D(16, 5, activation='relu', padding='same')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
drop1 = Dropout(0.3)(conv1)

pool1 = MaxPooling2D(pool_size=(2, 2))(drop1)

conv2 = Conv2D(24, 5, activation='relu', padding='same')(pool1)
conv2 = BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
drop2 = Dropout(0.3)(conv2)

pool2 = MaxPooling2D(pool_size=(2, 2))(drop2)

conv3 = Conv2D(32, 5, activation='relu', padding='same')(pool2)
conv3 = BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
drop3 = Dropout(0.3)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(drop3)

conv4 = Conv2D(40, 5, activation='relu', padding='same')(pool3)
conv4 = BatchNormalization()(conv4)
conv4 = Activation('relu')(conv4)
drop4 = Dropout(0.3)(conv4)

up5 = Conv2D(32, 3, activation='relu', padding='same')(
Conv2DTranspose(32, 5, activation='relu', padding="same", strides=2)(drop4))
merge5 = concatenate([drop3, up5], axis=3)
conv5 = Conv2D(32, 5, activation='relu', padding='same')(merge5)
drop5 = Dropout(0.6)(conv5)

up6 = Conv2D(24, 3, activation='relu', padding='same')(
Conv2DTranspose(24, 5, activation='relu', padding="same", strides=2)(drop5))
merge6 = concatenate([drop2, up6], axis=3)
conv6 = Conv2D(24, 5, activation='relu', padding='same')(merge6)
drop6 = Dropout(0.6)(conv6)

up7 = Conv2D(16, 3, activation='relu', padding='same')(
Conv2DTranspose(16, 5, activation='relu', padding="same", strides=2)(drop6))
merge7 = concatenate([drop1, up7], axis=3)
conv7 = Conv2D(16, 5, activation='relu', padding='same')(merge7)
drop7 = Dropout(0.6)(conv7)
conv8 = Conv2D(1, 1, activation='relu')(drop7)

model = Model(inputs=inputs, outputs=conv8)

opt = Adam()

model.compile(optimizer=opt, loss='mse', metrics=['accuracy'])

if pretrained_weights:
model.load_weights(pretrained_weights)

return model

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